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Justin Beslity -Thesis

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Justin Beslity -Thesis

  1. 1. i TRINITY COLLEGE BASELINE ALUMINUM AND CALCIUM ION CONCENTRATIONS BEFORE A CLEAR CUT IN THE WHITE MOUNTAINS NATIONAL FOREST, MAINE & NEW HAMPSHIRE, USA BY JUSTIN BESLITY A THESIS SUBMITTED TO THE FACULTY OF THE ENVIRONMENTAL SCIENCE PROGRAM IN CANDIDACY FOR THE BACCALAUREATE DEGREE WITH HONORS IN ENVIRONMENTAL SCIENCE ENVIRONMENTAL SCIENCE PROGRAM HARTFORD, CONNECTICUT 5/6/15
  2. 2. ii BASELINE ALUMINUM AND CALCIUM ION CONCENTRATIONS BEFORE A CLEAR CUT IN THE WHITE MOUNTAINS NATIONAL FOREST, MAINE & NEW HAMPSHIRE, USA BY JUSTIN BESLITY Honors Thesis Committee Approved: ______________________________________________ Professor Jonathan Gourley ______________________________________________ Professor Christoph Geiss ______________________________________________ Mr. Robert Colter Date: ________________________________________
  3. 3. iii Table of Contents 1) Abstract………………………………………………………………………………………iv 2) Acknowledgements………………………………………………….………………………..v 3) Introduction…………………………………………………………………………………..1 a) Aluminum………………………………………………………………………….………1 b) Calcium……………………………………………………………………………………2 c) Negative Processes Resulting from Lumber Harvesting……………….………………….3 d) Biological Factors Affecting Ion Concentrations…………………………………………4 e) Hubbard Brook Study……………………………………………………………………..4 4) Methods a) The White Mountains National Forest………………………………………………….....5 b) Mill Stone Site……………………………………………….…………………………….6 c) Douglas Brook Site………………………….…………………………………….…..…...7 d) Hogsback Site………………………………………………….………………………..…7 e) Site Maps……………………………………………………………………….….…........8 f) Baseline……………………………………………………………………………..……12 g) Sample Collection…………………………………………….…………..………...……12 h) Sample Processing…………………………………………………….…………………14 i) ICP – OES………………………………………………………………………………..14 j) Sample Confirmation…………………………………………………………………….14 5) Results a) Aluminum……………………………………….………………………………………..16 i) O Layer…………………………………………………….…………………………16 ii) B Layer……………………………………………………….………………………16 iii) Average Aluminum Concentrations and Elemental Interpolations…………….…….17 b) Calcium……………………………………………………….…………………….…….25 i) O Layer………………………………………………………….……………………25 ii) B Layer………………………………………………………………………….……25 iii) Average Calcium Concentrations and Elemental Interpolations…………………….26 c) GIS Interpolations………………………………………………………………………..34 6) Discussion………………………………………………………………….………………...34 7) References…………………………………………………………………………………...37
  4. 4. iv Acknowledgements: I would like to thank Professor Jonathan Gourley, for his support and patience throughout my time as his research assistant and student. I would also like to thank Robert Colter of the United States Forest Service, for his participation and help with the logistical preparation of our study. I would like to express my gratitude towards Professors Geiss and Douglass, who, throughout all hours of the day and night, provided critical knowledge necessary for the structure and content of this report, and mine own sanity. I would I would also like to share my appreciation for my fellow research partners, Lauren Tierney and Jack Agosta, involved in the Trinity College White Mountains National Forest Study – Ion Division, whose support was essential in the completion of this baseline analysis.
  5. 5. v Abstract The harvesting of timber can have negative effects on the concentrations of aluminum and calcium in the soil of forest ecosystems. Acid rain, which falls directly onto the soil in the absence of a canopy, disassociates these elements from bonds with the organic material in the soil, raising the average concentration of inorganic, toxic aluminum. Harvesting timber also increases erosion, which contributes to a loss of soil ions. In order to better understand how these processes influence forest floor ecosystems, this long term study will observe the changes in concentrations of aluminum and calcium. This is a preliminary baseline report on the concentrations of these elements prior to clear-cutting, which will provide the necessary information to access the flux of ions. O- and B-horizons from three sites across the White Mountains National Forest of New Hampshire and Maine were sampled using a transect method. Soil samples were analyzed using an Inductively Coupled Plasma – Optical Emission Spectrometer, in order to obtain aluminum and calcium concentrations in parts per million, which were used to calculate the average ion concentrations for each site per soil horizon. The average concentrations observed in the three sites fit into the United Stated Geological Survey’s range of aluminum and calcium soil concentrations. For Millstone, Douglas Brook and Hogsback, respectively, the average concentrations were: for aluminum in the O- horizon 2800 ± 630 ppm, 1900 ± 367 ppm, and 4000 ± 1092 ppm, for aluminum in the B- horizon 9600 ± 791 ppm, 7600 ± 844 ppm, and 10500 ± 2277 ppm, for calcium in the O-horizon 1400 ± 160 ppm, 1700 ± 263 ppm and 1100 ± 167 ppm, and for calcium in the B-horizon 150 ± 20, 160 ± 21 ppm and 130 ± 20 ppm.
  6. 6. 1 Introduction This report is the first installment of a study of the effects of timber harvesting on the aluminum and calcium concentrations in O- and B-soil horizons of three sites set to be harvested in the White Mountain National Forest of New Hampshire and Maine. The concentrations of these elements will be monitored annually for several years in order to observe the effects of the two primary mechanisms of ion loss - 1) soil erosion and 2) acid rain [Moore, 2005]. This study will increase our understanding of how the management practices of timber harvesting and WMNF forest managers impact the soils of clear-cut areas. The mechanisms that influences baseline aluminum and calcium concentrations will be detailed and analyzed to see how they affect the concentrations at the three different sites. By cataloging the concentrations over a period of years, we will be able to determine how aluminum and calcium concentrations in the O and B soil horizons change post clear-cut. This report will focus on the establishment of this pre- cut baseline data in order to provide the framework for the following years of comparison. Aluminum: In healthy forest soils, aluminum is bound to the organic material in the substrate. However, when aluminum dissociates due to increase acidity, it becomes a soluble plant toxin. According to the EPA, acid rain has a typical pH of 4.0, well below the acid dissociation value of 4.7 for aluminum. As a free, inorganic ion, aluminum becomes a limiting factor for plant growth [Kochian, 1995]. When active, aluminum interrupts cellular functions and prevents the roots of plants from developing, depriving the plant of the nutrients necessary for survival. Additionally, when ionic compounds containing aluminum become soluble, they tend to bond with free calcium ions, which can play a disruptive role in the intake of calcium for plants [Foy, 1988]. In undisturbed ecosystems, aluminum does not usually play a significant role, but in highly disturbed areas, where the soil has been exposed to large quantities of acid rain, without any canopy cover, aluminum can build up to concentrations capable of disrupting plant growth [Wright, 1989]. Aluminum is the most common metal of the Earth’s crust and is, therefore, often found in large concentrations in soil. The most common source for aluminum in Northeastern watersheds is mineral weathering, though minute concentrations of atmospheric deposition can play important role [Lal, 2006]. Though there is a large natural variation in aluminum in
  7. 7. 2 different soil types, the United States Geological Survey placed the average soil aluminum concentration at 72,000 ppm, with a range of 700 ppm to values greater than 100,000 ppm. Two other studies assimilated into the USGS report placed the average concentration at 71,300 ppm, and a range between 10,000 ppm and 60,000 ppm respectively [Shacklette and Boerngen, 1984]. Calcium: Calcium is a well-known soil nutrient. It plays a critical role in the building of cell walls and membranes, coordinating responses to developmental and environmental cues, is a counter- cation for anions in the vacuole, and represents between 0.3-1.5% of the total mass of most angiosperms [Likens et al., 1998] [White and Broadley, 2003]. Calcium is also responsible for nitrate uptake, starch metabolism and enzyme activity [Likens et al., 1998]. Hardwood species store roughly 70% of their total calcium concentration in their branches, bark and roots, which are generally left behind during a timber harvest [Likens et al., 1998]. Calcium is generally found in adequate concentrations in the soils of deciduous forests, however, when enough soil is removed due to runoff, calcium can begin to quickly leave the system [Federer et al., 1989]. Arial deposition of calcium, which can play a large role in the calcium cycle of forest ecosystems, is relatively low in New England, and the concentrations acquired at the Hubbard Brook experimental forest are among the lowest for any region in the United States [Likens et al., 1998] The soils of the New Hampshire and Maine are low in clay content and high in acidity, characteristic of soil which have been subjected to glacial scouring. In an equilibrium state, most calcium in an ecosystem is either absorbed into the roots of plants, used to produce biomass, or in leaf litter and other organic material decomposing on the forest floor. A small amount of calcium enters the system through the bedrock, primarily through the weathering of plagioclase, a common mineral in the White Mountains National Forest, and a similar amount exits the system naturally through runoff [Likens et al., 1998]. Due to the limited mineral weather and base poor nature of the New Hampshire and Maine bedrock, removal of timber from forest ecosystem can have a profound effect on the availability of calcium. In most ecosystems, exchangeable calcium concentrations in the soil range from 300 ppm to 5000pm. In sandy soils, such as the B soil horizon, calcium concentrations are lower, generally between 400 ppm and 600 ppm, while in finer soil, such as the O horizon, concentrations are higher, ranging from 600 to 1000 ppm [Keeling and Schulte, 2004]. In a
  8. 8. 3 USGS mapping of the concentrations of calcium across the United States, it was found that the average concentration was 24,000 ppm, through the values ranged from 100 ppm to 320,000 ppm. Similar studies incorporated by the USGS reported average calcium concentrations at 13,700 ppm and 7,000 ppm [Shacklette and Boerngen, 1984]. Negative Processes Resulting from Timber Harvesting: Clear cutting is a method of harvesting timber, commonly used throughout the world, in which all the trees of a particular stand are felled. The trunks of the trees are then transported offsite using heavy machinery. If soils begin to erode, critical soil nutrients are lost rapidly to the surface or ground water system. This process is dramatically increased if the soil is left bare, and rain is allowed to scour the land scape. Barren land is highly susceptible to runoff, and loses up to 100 times more soil than forested lands in similar habitats [Pimentel et al., 1995]. Slope is an important factor when predicting the effects of soil erosion, as the amount of energy required to move soil is substantially decreased with an increase of slope. Barren plots at a slope of 20% are capable of losing 400 tons ha-1 yr-1 of soil, compared to 3 tons ha-1 yr-1 on barren lands with a slope less than 1% [Pimentel et al., 1995]. For these purposes, management practices are extremely important [Patric, 1976] [Stone, 1973] [Dahlgren and Driscoll, 1994]. During a clear-cut, the branches, and any other undesirable part of the tree, are removed on site to ease transport. This left over material is commonly called slash, and is often strewn across the clear-cut site. In theory, along with being a convenient method of slash disposal, this process is meant to secure the soil in place in the absence of the trees. Rain that hits the slash prior to striking the soil loses energy, and therefore transfers less towards the movement of soil. Slash also creates boundaries, which slows the downward flow of water, catching soil, and decreasing the waters ability to carry material [Pimentel et al., 1995]. The decomposition of the slash also replenishes the soil with nutrients that may have lost due to runoff [Moore, 2005]. Without the presence of strong acid anions, cation leaching in in forest soils occurs mostly in the O horizon, which is driven by the organic acids created during the decomposition of organic material. This would suggest higher levels of cation depletion in the O horizon, especially for aluminum ions[Driscoll et al., 2001]. However, with the advent of the Industrial Revolution and the burning of fossil fuels, Nitrogen and Sulfur Oxides have been added to the atmosphere, where they react with H2O and create strong acids. These acids are then deposited
  9. 9. 4 through acid rain, which poses a significant threat to soil ion concentrations in the O horizon. To some degree, soil cations, such as calcium, are capable of acting as a buffer against the effects of acid rain. As long as the exchangeable base cation concentration remains above 20% of the total cation exchange capacity, the forest floor is able to neutralize the effects of atmospheric deposition of strong acids [Driscoll et al., 2001]. However, when these forest ecosystems are disturbed and eroded, especially through the harvesting and removal of the canopy, acid raid can have a profound effect on the availability of ions. Acid rain causes the disassociation of several macro-nutrients for plants, such as potassium and calcium, and allows for a greater amount of these nutrients to be carried away through run off. This process also drives the shift from higher concentrations of non-toxic, organic forms of aluminum to inorganic forms, which are plant toxins [Krug and Frink, 1983][Mulder and Stein, 1994]. Biological Factors Affecting Ion Concentrations The tree species of the northeastern forest ecosystems can have a profound effect on chemistry of the soil. In particular, the Sugar Maple (Acer saccharum) and the Eastern Hemlocks (Tsuga canadensis) are both capable of increasing the local concentrations of specific ions. Higher concentrations of calcium are found in the soils surrounding Sugar Maples, which is potentially caused by a relatively higher concentration of calcium in the leaves of A. saccharum, and by a relatively larger production of leaf litter [Finzi et al., 1998]. Samples collected near Sugar Maples can be expected to be higher in calcium. Eastern Hemlocks contain high concentrations of tannins in their leaves and bark, which prevent decomposition. This is likely to lower the local pH of the soils surrounding T. Canadensis, which increases the solubility of aluminum [Woods, 2000]. Samples collected near the Eastern Hemlock can be expected to be higher in aluminum [Finzi et al., 1998] The Hubbard Brook Study The Hubbard Brook Experimental Study was conducted in a small water shed, located in the western section of the White Mountains National Forest, 43”56’N, 71”45’W. This study examined the effects of clearcutting on the O- and B- soil horizons in order to better understand the natural response to timber harvesting. It was found that clearcutting caused significant leaching of soil nutrients and acidification of both the O- and B- soil horizons, however the O-
  10. 10. 5 horizon suffered greater loss of soil nutrient concentrations. Nitrification in the soil horizons caused the buildup of Nitric Acid, which was largely neutralized due to the leaching of basic cations. Calcium concentrations in the O-horizon decreased from a baseline value of 80 µmol L-1 to 55 µmol L-1 in the first year, then down to 35 µmol L-1 the second year. Following the initial decline, calcium concentrations began to rise back to reference levels in the fourth and fifth years of the study. Calcium concentrations in the B-horizon, which were lower than those seen in the O-horizon, experienced an initial increase in calcium ion concentrations from 40 µmol L-1 to 60 µmol L-1 . However, in the third year, calcium concentrations fell by half and stabilized for the remainder of the study. Aluminum reached potentially toxic concentrations for plant life due to the increased acidity. Both organic and inorganic aluminum ion concentrations were analyzed in the O- and B-horizons, however, organic aluminum was unaffected by the timber treatment. Organic aluminum was found in higher concentrations in the O-horizon than in the B-horizon, while inorganic aluminum was found in higher concentrations in the B-horizon than in the O-horizon. The toxic, inorganic form of aluminum experienced a 100% increase of concentration in the B-horizon, from 20 µmol L-1 to 40 µmol L-1 , in the second year of the study. Inorganic aluminum concentrations then fell back down to reference concentrations. The impact of the clear-cut was most significant after the second year post harvest, and ion nutrient concentrations began to return to baseline concentrations in the fourth and fifth year of the study [Dahlgren and Driscoll, 1994]. By comparing our study of soil ion concentrations to the experimental study of the Hubbard Brook watershed, we can gain a more comprehensive understanding of the natural responses of the forest floor ecosystem to clear cutting. Methods The White Mountains National Forest: This study was conducted in the White Mountains National Forest of New Hampshire and Maine. This is a mountainous area, with mainly Hemlocks, White Pines and Northern Hardwoods representing the tall woody fauna. Sample collection occurred at three sites across the White Mountain National Forest (WMNF). The Millstone Site was located in the eastern- most section of the WMNF, in Maine, the Douglass Brook Site was located more centrally in the
  11. 11. 6 WMNF, in New Hampshire, and the Hogsback site was located to the far west of the WMNF (Fig. 1). The sites vary slightly in topography and elevation, though the forests consisted mainly of mixed northern hardwood species [Sugar maple (Acer saccharum), American Beech (Fagus grandifolia) and Yellow Birch (Betula alleganiensis)] and Eastern Hemlocks (Tsuga Canadensis) at lower elevations. The sampling sites were located between one and three miles from the nearest accessible public road. Each sampling site was located in a different management district for the USFS, which may result in different site conditions due to the geography of the area. The Appalachian mountain range runs through the White Mountains National Forest, which influences the weather patterns across the three sites. Due to the increases in elevation, the air is compressed as the jet stream moves from west to east, which causes an increase in rain fall on the western section of the White Mountains National Forest. Increases in rain fall to the west would likely lead to increased quantities of strong acids in the soils, as much of the acids result from the pollution from the coal-fired power plants of the Mid-West [Krug and Frink, 1983]. The sites were selected by representatives of the United States Forest Service in order to access each of these different management practices. Millstone Site: The Millstone site is located in the Androcoggin Ranger District, outside of Bethel, Maine, off of Flat Road, 44˚19’36.97” N, 70˚48’56.06” W (Fig. 2). The sale was first sampled on June 24th , 2013. This site is located between the elevations of 800-1000 feet, roughly 12 acres in area and has an average slope of 19% that faced towards the northwest. At the lowest elevation of the three sites, Millstone has a noticeably higher percentage of Eastern Hemlocks located with the study site, which could have the potential to locally lower the pH of the soil [Finzi et al., 1998]. Millstone sits on a granodiorite bedrock formed in the siluro- devonian period. An extensive network of logging roads has been established throughout the area, and there are several small logging sales adjacent to the Millstone sale in every direction. Six transects were taken across the sale, with 13 samples taken outside the logging boundaries for this site, and 37 taken within.
  12. 12. 7 Douglas Brook Site: The Douglas Brook site is located in the Saco Ranger District, outside of Bartlett, New Hampshire off of Bear Notch Road, 44˚1’52.34” N, 71˚19’8.17” W (Fig. 3). The sale was first sampled on June 25th , 2013. This site is located between the elevations of 1600-1700 ft., is roughly 12 acres in area and has an average slope of 21% that faced towards the west. This site has the highest slope variability, 25% on the southern side of the site, and only 16% on the northern side. The site lies on the White Mountains Batholith, a large granitic intrusion in the center of the White Mountains National Forest. A well-constructed logging road was built from Bear Notch Road to the sale, roughly a half mile in length, which fords the nearby stream. Six transects were taken across the Douglas Brook site, with eight samples taken outside the logging boundaries for this site, and 35 samples taken within. Hogsback Site: The Hogsback site is located in the Pemigewasset Ranger District, outside of Benton, New Hampshire off of the Mt. Moosilauke Highway, 44˚1’34.88” N, 71˚54’57.42” W (Fig. 4).. The sale was first sampled on June 26th , 2013. The site is located between and the elevation of 2000-2150 ft., roughly 13 acres in areas and has an average slope of 21% that faces towards the west. Hogback is the highest in elevation of the three sites. The site lies on the Bronson Hill Anticlinorium, a metamorphosed sedimentary layer from the Ordovician period. It boarders the Blueberry Mountain Hiking Trail to both the south and east, which is also the most convenient route to the logging site. Large logging roads have been constructed around the hiking trails that were already in place to move timber out of the Hogsback site Three transects were taken across the sale, with four samples taken outside the logging boundaries for this plot, and 31 samples taken within.
  13. 13. 8 Figure 1: Overview map of the White Mountains National Forest of New Hampshire and Maine. Study site are surrounded by red rectangles. Map by Justin Beslity and Daniel Hong
  14. 14. 9 Figure 2: Site map for Millstone logging site. Site is marked in red with a light blue outline. Sample locations are marked with black dots.
  15. 15. 10 Figure 3: Site map for Douglas Brook logging site. Site is marked in red with a light blue outline. Sample locations are marked with black dots.
  16. 16. 11 Figure 4: Site map for Hogsback logging site. Site is marked in red with a light blue outline. Sample locations are marked with black dots.
  17. 17. 12 Baseline: Establishing a baseline concentration for a large forested site required an appropriate method in order to accurately obtain a mean sample value. The natural variation in ion concentrations across sites roughly 12 acres in area can be quite substantial. In order to capture this variation, we collected samples in transects spanning the length and width of the sites proposed for cutting. Between two and five trials were analyzed for each soil sample, depending on the precision, which were then averaged in order to find a mean concentration per sample site. In order to visually represent the potential concentrations between the known sample values, an interpolation was created on ArcGIS, using spatial analysis. These maps are excellent visual aids for conceptualizing the site wide concentrations, however, it must be noted that the concentrations outside of the known sample values are hypothetical, calculated by their proximity to surrounding known values. Sample collection: Sampling of the sites was conducted using a transect pattern that covered roughly the entire plot, including several points per plot that were outside the plot boundaries. The boundaries of each site were marked in spray paint on the sides of tress with two bright orange, slanted lines, along with the designated site number. Transects were generally placed perpendicular the nearest logging road or trail, in order to maintain a sense of orientation. Sampling occurred in both the O-horizon at the surface, and the B-horizon, generally about a 20- 30 cm beneath the surface. The soil horizon was identified by color and texture. Soil from the O-horizon was generally dark brown in color, and laden with roots and other organic material. Soil from the B-horizon was generally a rusty red color, and courser in texture, composed of larger, sandier grains (Fig. 5).
  18. 18. 13 Figure 5: Depiction of the different soil horizons. http://en.wikipedia.org/wiki/Soil_horizon#/media/File:SOIL_PROFILE.png
  19. 19. 14 The respective soil was placed in Whirl-Paks, and stored in a cooler. It was noted whether the sample was collected from inside or outside of the logging boundaries. The location of each sampling site was documented using a Garmin Montana 650 GPS, and assigned a letter and numeral code representing its location in the transect array. Sample processing: Upon return to the lab, the samples were immediately freeze dried, in order to preserve the state of the soil. The process of freeze drying required roughly 24-48 hours, depending on the quantity of water in the sample. The dry samples were then cut up using ceramic scissors, as opposed to metal, in order to prevent any additional ions from entering the samples through contamination. A weak acid digest was required for analysis in the Inductively Coupled Plasma - Optical Emission Spectrometer (ICP – OES). The weak acid solution consisted of 10 ml of 0.6 M Hydrochloric acid and 0.500 g of the soil sample, shaken for 1 hour before processing. ICP – OES: The ICP – OES was calibrated using CPI International Soil Mix Standard, which contained concentrations of 700 ppm aluminum and 125 ppm calcium. The calibration curve consisted of a Blank, 0%, and a High Standard, 70% the concentration of Soil Mix Standard. A quality control check was set at 30% the concentration of the standard. Trials of the same sample were run on different days in order to ensure that results were not skewed by slight changes in calibrations of different sessions. The calibration process was preformed after roughly 10-15 trial runs, in order to prevent any errors in the calibration curve. Sample Concentration Confirmation: For each sample location, the measured ion concentration was accepted when at least two non-consecutive trial measurements yielded concentrations within a 10% error. If the first two trials of sample fell outside of the established 10% error, two more samples were prepared and analyzed. The confirmed sample trials were then averaged together to form a mean concentration value for that sample of soil horizon.
  20. 20. 15 0 200 400 600 800 1000 1200 0 1 2 3 4 5 AluminumConcentration(ppm) Trial Number HB C33 O Layer Figure 6: Example of outlier elimination. Trials 2, 3 & 4 are within a 10% error of each other and were included in the results, while Trial 1 falls far below the 10% error cutoff, as was eliminated from the results.
  21. 21. 16 Results Aluminum: The concentration of aluminum in the O- and B- soil horizons varied between the three sites. The Hogsback had the highest concentrations in both the O- and B- horizons, followed by the Millstone site, then the Douglas Brook site. O Horizon: The average concentrations of aluminum in the O-horizon were plotted with their standard error in order to establish a 95% confidence interval between the mean site concentrations (Fig. 7). The Millstone site had an average aluminum concentration of 2800 ± 630 ppm (Fig. 8). The Douglas Brook site had an average aluminum concentration of 1900 ± 370 ppm (Fig. 9). The Hogsback site had an average aluminum concentration of 4000 ± 1100 ppm (Fig. 10). B Horizon: The average concentrations of aluminum in the B-horizon were plotted with their standard error in order to establish a 95% confidence interval between the mean site concentrations (Fig. 11). The Millstone site had an average aluminum concentration of 9600 ± 790 ppm (Fig. 12). The Douglas Brook site had an average aluminum concentration of 7600 ± 840 ppm (Fig. 13). The Hogsback site had an average aluminum concentration of 10500 ± 2300 ppm (Fig.14).
  22. 22. 17 0 1000 2000 3000 4000 5000 6000 Aluminum(ppm) Millstone Hogsback Douglas Brook Figure 7: Average aluminum concentrations in the O-horizon, in parts per million, of the three timber sites. The error bars represent the associated Standard Error
  23. 23. 18 Figure 8: Spatial interpolation of the concentration of aluminum in the O-horizon of the Millstone site.
  24. 24. 19 Figure 9: Spatial interpolation of the concentration of aluminum in the O-horizon of the Douglas Brook site.
  25. 25. 20 Figure 10: Spatial interpolation of the concentration of aluminum in the O-horizon of the Hogsback site.
  26. 26. 21 0 2000 4000 6000 8000 10000 12000 14000 Aluminum(ppm) Millstone Hogsback Douglas Brook Figure 11: Average aluminum concentrations in the B-horizon, in parts per million, of the three timber sites. The error bars represent the associated Standard Error
  27. 27. 22 Figure 12: Spatial interpolation of the concentration of aluminum in the B-horizon of the Millstone site.
  28. 28. 23 Figure 13: Spatial interpolation of the concentration of aluminum in the B-horizon of the Douglas Brook site.
  29. 29. 24 Figure 14: Spatial interpolation of the concentration of aluminum in the B-horizon of the Hogsback site.
  30. 30. 25 Calcium: The concentration of calcium in the O- and B- soil horizons varied between the three sites. The Douglas Brook site had the highest average concentration, followed by the Millstone site, then the Hogsback site. O Horizon: The average concentrations of calcium in the O-horizon were plotted with their standard error in order to establish a 95% confidence interval between the mean site concentrations (Fig. 9). The Millstone site had an average calcium concentration of 1400 ± 160 ppm (Fig. 17). The Douglas Brook site had an average calcium concentration of 1100 ± 170 ppm (Fig. 18). The Hogsback site had an average calcium concentration of 1700 ± 260 ppm (Fig. 19). B Horizon: The average concentrations of calcium in the B-horizon were plotted with their standard error in order to establish a 95% confidence interval between the mean site concentrations (Fig. 11). The Millstone site had an average calcium concentration of 150 ± 20 ppm (Fig. 20). The Douglas Brook site had an average calcium concentration of 160 ± 20 ppm (Fig. 21). The Hogsback site had an average calcium concentration of 130 ± 20 ppm (Fig. 22).
  31. 31. 26 0 500 1000 1500 2000 2500 Calcium(ppm) Millstone Hogsback Douglas Brook Figure 15: Average calcium concentrations in the O-horizon, in parts per million, of the three timber sites. The error bars represent the associated Standard Error
  32. 32. 27 Figure 16: Spatial interpolation of the concentration of calcium in the O-horizon of the Millstone site.
  33. 33. 28 Figure 17: Spatial interpolation of the concentration of calcium in the O-horizon of the Douglas Brook site.
  34. 34. 29 Figure 18: Spatial interpolation of the concentration of calcium in the O-horizon of the Hogsback site.
  35. 35. 30 0 20 40 60 80 100 120 140 160 180 200 Calcium(ppm) Millstone Hogsback Douglas Brook Figure 19: Average calcium concentrations in the B-horizon, in parts per million, of the three timber sites. The error bars represent the associated Standard Error.
  36. 36. 31 Figure 20: Spatial interpolation of the concentration of calcium in the B-horizon of the Millstone site.
  37. 37. 32 Figure 21: Spatial interpolation of the concentration of calcium in the B-horizon of the Douglas Brook site.
  38. 38. 33 Figure 13: Spatial interpolation of the concentration of aluminum in the B layer of the Hogsback site. Figure 14: Spatial interpolation of the concentration of aluminum in the O layer of the Hogsback site. Figure 22: Spatial interpolation of the concentration of calcium in the B-horizon of the Hogsback site.
  39. 39. 34 GIS Interpolations In order to better visualize the potential site wide concentrations, and elemental interpolation was created using spatial analyst tools in ArcGIS. The known ion concentrations from soil sample were joined to their respective GPS marking for each sample location. The Spline with Barriers analyst tool was used in order to construct an interpolation from the point data using a minimum curvature spline technique. This spline technique creates a grided array in which each cell is calculated based on the weighted summation of 12 neighboring cells compared to the known values of the central target cells, or the imputted point data [Terzopoulos, 1988]. The barrier was drawn tightlty along the outermost samples in order to minimize any generation of data without sufficiet neighboring cells with known concentrations. This causes the interpolation to rise or fall on the edges, based on the increasing or decreasing trends of the surrrounding known concentrations. The barrier was used again to isolate the interpolated raster image, using the tool Extract by Mask. The raster symbology was switched from a continous Discrete Color ramp, which provided a smooth interpolation of the ion concnetrations, to a Calssified setting with maually entered breaks, which created a contoured appearance. Discussion With the achievement of a 10% error between the trials of the each sample, there is enough confidence in the concentrations observed throughout the three sites in order to establish a baseline. In addition, with the average soil layer concentrations falling with the USGS national range for soil ion concentrations, which adds plausibility to our sample concentrations. The most striking relationship between the three study sites was the inverse relationship between aluminum and calcium. Hogsback had the highest average concentration of aluminum of the three sites, yet it had the lowest average calcium concentration. Douglas Brook, on the other hand, had the highest concentration of calcium, and the lowest concentration aluminum. Millstone, fell in the middle of the three sites in both aluminum and calcium concentrations. This would suggest that there is a factor connecting the concentrations of these ions. In acidic soils, aluminum becomes soluble in water and binds more aggressively than calcium, which may result in a replacement of calcium with aluminum [Goyer, 1997]. It is possible that the Hogsback site, closest to the input of the acid pollution, has the highest value of pH. As the air moves from west to east, it compresses over the White Mountains, which increase rain fall on the
  40. 40. 35 western side of the National Forest [Krug and Frink, 1983]. This would partially account for the elevated amount of aluminum present in the soil horizons of Hogsback, as increased acidity causes aluminum concentrations to rise. If acid rain were the sole cause of increased aluminum, we should see the lowest concentration of aluminum at the Millstone site, which is located furthest to the east. A potential explanation for the increased aluminum at this site would be the presence of the Eastern Hemlock. This tree, which was not present at the Hogsback or Douglas Brook site, is capable of locally raising the pH of the soil, which increases the concentration of aluminum and decreases the concentration of calcium [Woods, 2000]. Sugar Maples could also play a large role in raising the calcium concentrations of the different sites. The relatively high concentrations of calcium found the leaves of A. saccharum locally raise the calcium concentrations of the O-horizon [Likens et al., 1998] [Finzi et al., 1998]. Sugar Maples were quite common at each site, which would suggest that any effect observed by the presence of the tree would be present at each site. Locally however, these biological may have strong impact on the concentrations of aluminum and calcium, and may account for the large amount of standard deviation found with any of the three sampling sites. Calcium was found in higher concentrations in the O-horizon than in the B-horizon in every site. This suggests that there is a very tight cycle between the calcium stored in biomass and the calcium found in the O-horizon of the soil. Large trees draw calcium up from the lower soil horizons, distribute it throughout the new growth each season, which is then returned to the O-horizon and decomposed [White and Broadley, 2003]. The flux of calcium between the biota and the O-horizon is fairly stable on a seasonal basis, with smaller concentrations continually running of and being replenished by the deeper soil horizons [Likens et al., 1998]. Due to inexperience with soil collection, A-horizon soil may have been collected in place of O-horizon soils in some locations, which may have increased the standard deviation of our sampling average. This may prove problematic when conducting statistics to test for differences in soil ion concentrations in the following years, depending on the amount of calcium found in the A- horizon compared to the O-horizon. If the A-horizon was sampled frequently and had a significantly different concentration of calcium compared to the O-horizon, this may be responsible for a portion of the large standard deviation. In order to avoid similar problems with future sampling, the O-horizon should be taken directly below the leaf litter, before contact with
  41. 41. 36 the A-horizon. Sample collection should also continue outside the prescribed treatment of the site, which will allow for control data throughout the experiment. If similar results are found year after year in sample locations outside of the clear cut, we can be more confident in our initial baseline concentrations. The bedrock geology of the three sites may play the largest role in determining the concentrations of the different metals in the soil. Each of the three sites lies on either a granitic or metamorphosed bedrock, which would indicate a slower rate of mineral weathering [Lal, 2006]. The fastest weathering rates seen in the northeastern United States occurs in Vermont, where a calcareous schist adds about 3380 mol ha-1 yr-1 of calcium to the soil each year. The slowest weathering seen in the northeastern United States occurs in New Hampshire, where a bedrock of schist adds only 77 mol ha-1 yr-1 of calcium. The mineral weather rates of the three sites most likely falls between the range of 100 mol ha-1 yr-1 of calcium and 500 mol ha-1 yr-1 of calcium, based on comparable bedrock weathering rates in the northeastern United States [Likens et al., 1998]. Loss of soil ions positively correlates with surrounding streamflow, signifying a higher loss of nutrients during periods of higher rainfall. However, net loss of most soil ions do to erosion in undisturbed, northeastern forest ecosystems is less than the net gain of ions due to mineral weather and aerial deposition [Likens et al., 1998]. The large difference in concentrations between the timber sites could very well be due to the different mineral weathering rates, which would have a strong influence on the amount of ions found in the soil. The importance of a baseline concentration in studies that assess long term changes cannot be understated. By knowing values of aluminum and calcium both inside and outside timber treatments, we can have a better understanding of how these ions are altered by a clear- cut of the forest. Based on the literature cited in this report, the effects of clear cutting lower concentrations of calcium, while simultaneously increasing concentrations of inorganic, toxic aluminum, both of which have potential negative effects on plant growth. Long term studies such as this, and the Hubbard Brook Experimental Forest, help us better understand the time frame in which it takes a natural forest ecosystem to recover from a complete timber harvest.
  42. 42. 37 References Dahlgren, R. A. and Driscoll, C. T. (1994), The effects of whole-tree clear-cutting on soil processes at the Hubbard Brook Experimental Forest, New Hampshire, USA, Plant Soil, 158(2), 239–262. Driscoll, C. T., Lawrence, G. B., Bulger, A. J., Butler, T. J., Cronan, C. S., Eagar, C., Lambert, K. F., Likens, G. E., Stoddard, J. L. and Weathers, K. C. (2001), Acidic Deposition in the Northeastern United States: Sources and Inputs, Ecosystem Effects, and Management Strategies, BioScience, 51(3), 180–198. Federer, C. A., Hornbeck, J. W., Tritton, L. M., Martin, C. W., Pierce, R. S. and Smith, C. T. (1989), Long-term depletion of calcium and other nutrients in eastern US forests, Environ. Manage., 13(5), 593–601. Finzi, A. C., Canham, C. D. and Van Breemen, N. (1998), Canopy Tree–Soil Interactions Within Temperate Forests: Species Effects on pH and Cations, Ecol. Appl., 8(2), 447–454. Foy, C. D. (1988), Plant adaptation to acid, aluminum‐toxic soils, Commun. Soil Sci. Plant Anal., 19(7-12), 959– 987. Goyer, R. A. (1997), Toxic and Essential Metal Interactions, Annu. Rev. Nutr., 17(1), 37–50. Keeling, K. A., and Schulte, E. E. (2004), Soil and Applied Calcium, Underst. Plant Nutr., A2523. Kochian, L. V. (1995), Cellular Mechanisms of Aluminum Toxicity and Resistance in Plants, Annu. Rev. Plant Physiol. Plant Mol. Biol., 46(1), 237–260. Krug, E. C., and Frink, C. R. (1983), Acid Rain on Acid Soil: A New Perspective, Am. Assoc. Adv. Sci., 221(4610), 520–525. Lal, R. (2006), Encyclopedia of Soil Science, CRC Press. Likens, G. E., Buso, D. C., Siccama, T. G., Johnson, C. E., Lovett, G. M., Fahey, T. J., Reiners, W. A., Ryan, D. F., Matin, C. W. and Bailey, S. W. (1998), The biogeochemistry of calcium at Hubbard Brook, Biogeochemistry, 41(2), 89–173. Moore, R. (2005), Final Environmental Impact Statement, USDA, Milwaukee, WI. Mulder, J. and Stein A. (1994), The solubility of aluminum in acidic forest soils: Long-term changes due to acid deposition, Geochim. Cosmochim. Acta, 58(1), 85–94. Patric, J. H. (1976), Soil Erosion in the Eastern Forest, J. For., 74(10), 671–677. Pimentel, D., Harvery, C., Resosudarmo, P., Sinclair, K., Kurz, D., McNair, M., Crist, S., Shpritz, L., Saffouri, R. and Blair, R. (1995), Environmental and economic costs of soil erosion and conservation benefits, Sci. Wkly. USA. Shacklette, H. T. and Boerngen, J. G. (1984), Element Concentrations in Soils and Other Surficial Material of the Conterminous United States, U. S. Gov. Print. Off. Stone, E. (1973), The impact of timber harvest on soils and water, Report of the Prsident’s Advisory Panel on Timber and the Environment, US Government Printing Office. Terzopoulos, D. (1988), The computation of visible-surface representations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4), 417–438.
  43. 43. 38 White, P. J. and Broadley, M. R. (2003), Calcium in Plants, Ann. Bot., 92(4), 487–511. Woods, K. D. (2000), Dynamics in late-successional hemlock–hardwood forests over three decades, Ecology, 81(1), 110–126. Wright, R. J. (1989), Soil aluminum toxicity and plant growth, Commun. Soil Sci. Plant Anal., 20(15-16), 1479– 1497.

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